
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class 				UpperCAmelCase :




	'''simple docstring'''





	snake_case_          =	42
	snake_case_          =	42





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					int					):
					__A       =					[[] for _ in range(A					)]
					__A       =					size






	def __getitem__(			self   :					str				,A   :					int					):
					return iter(self._graph[vertex]					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return self._size






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					int				,A   :					int				,A   :					int					):
					if weight not in (0, 1):
									raise ValueError("Edge weight must be either 0 or 1."					)

					if to_vertex < 0 or to_vertex >= self.size:
									raise ValueError("Vertex indexes must be in [0; size)."					)

					self._graph[from_vertex].append(Edge(A				,A					)					)







	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					int				,A   :					int					):
					__A       =					deque([start_vertex]					)
					__A       =					[None] * self.size
					__A       =					0

					while queue:
									__A       =					queue.popleft()
									__A       =					distances[current_vertex]
									if current_distance is None:
													continue

									for edge in self[current_vertex]:
													__A       =					current_distance + edge.weight
													__A       =					distances[edge.destination_vertex]
													if (
													    isinstance(A				,A					)
													    and new_distance >= dest_vertex_distance
													):
																	continue
													__A       =					new_distance
													if edge.weight == 0:
																	queue.appendleft(edge.destination_vertex					)
													else:
																	queue.append(edge.destination_vertex					)

					if distances[finish_vertex] is None:
									raise ValueError("No path from start_vertex to finish_vertex."					)

					return distances[finish_vertex]


if __name__ == "__main__":
					import doctest

					doctest.testmod()








'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


